Dignity in the Terminally Ill: Revisited
Bibliographic record
Abstract
BACKGROUND: Several studies have been conducted examining the notion of dignity and how it is understood and experienced by people as they approach death. OBJECTIVE: The purpose of this study was to use a quantitative approach to validate the Dignity Model, originally based on qualitative data. DESIGN: Themes and subthemes from the Dignity Model were used to devise 22 items; patients were asked the extent to which they believed these specific issues were or could be related to their sense of dignity. RESULTS: Of 211 patients receiving palliative care, "not being treated with respect or understanding" (87.1%) and "feeling a burden to others" (87.1%) were the issues most identified as having an influence on their sense of dignity. All but 1 of the 22 items were endorsed by more that half of the patients; 16 items were endorsed by more than 70% of the patients. Demographic variables such as gender, age, education, and religious affiliation had an influence on what items patients ascribed to their sense of dignity. "Feeling life no longer had meaning or purpose" was the only variable to enter a logistic regression model predicting overall sense of dignity. CONCLUSIONS: This study provides further evidence supporting the validity of the Dignity Model. Items contained within this model provide a broad and inclusive range of issues and concerns that may influence a dying patient's sense of dignity. Sensitivity to these issues will draw care providers closer to being able to provide comprehensive, dignity conserving care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".